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DSLIC: A Superpixel Based Segmentation Algorithm for Depth Image

机译:DSLIC:深度图像的基于SuperPixel的分割算法

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Limited illumination outdoor and indoor environment leads to the lack of object's color information. Faced with this situation, it is not always possible to generate superpixel by using RGB or LaB features. To tackle this scenario, we propose a superpixel generation algorithm solely on depth image. We aim the semantically-incoherent superpixel problem on depth image, caused by identical depth value in the vicinity of the border. Our algorithm is an adaptation of Simple Linear Iterative Clustering (SLIC) with a novel utilization of depth and gradient direction as an alternate of LaB color space features. Our novel approach is demonstrated perform favorably to over-segment large planar area in an unlit environment.
机译:户外和室内环境有限,导致对象的颜色信息缺乏。面对这种情况,通过使用RGB或实验室功能并不总是可以生成SuperPixel。为了解决这一场景,我​​们提出了一个超顶像素生成算法,仅在深度图像上。我们的目标是在边界附近的相同深度值引起的深度图像上的语义 - 不连贯的超像素问题。我们的算法是简单的线性迭代聚类(SLIC)的适应性,具有深度和梯度方向的新颖利用,作为实验室色彩空间特征的替代。我们的新方法被证明在不利于环境中对超大平面区域有利的。

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